Daniel Tse
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Explore the profile of Daniel Tse including associated specialties, affiliations and a list of published articles.
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14
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1470
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Recent Articles
1.
Friedewald S, Sieniek M, Jansen S, Mahvar F, Kohlberger T, Schacht D, et al.
Breast Cancer Res Treat
. 2025 Jan;
PMID: 39881074
Purpose: Many breast centers are unable to provide immediate results at the time of screening mammography which results in delayed patient care. Implementing artificial intelligence (AI) could identify patients who...
2.
Kazemzadeh S, Kiraly A, Nabulsi Z, Sanjase N, Maimbolwa M, Shuma B, et al.
NEJM AI
. 2025 Jan;
1(10.
PMID: 39823016
Background: Using artificial intelligence (AI) to interpret chest X-rays (CXRs) could support accessible triage tests for active pulmonary tuberculosis (TB) in resource-constrained settings. Methods: The performance of two cloud-based CXR...
3.
Kiraly A, Cunningham C, Najafi R, Nabulsi Z, Yang J, Lau C, et al.
Radiol Artif Intell
. 2024 Mar;
6(3):e230079.
PMID: 38477661
Purpose To evaluate the impact of an artificial intelligence (AI) assistant for lung cancer screening on multinational clinical workflows. Materials and Methods An AI assistant for lung cancer screening was...
4.
Lee C, Willis A, Chen C, Sieniek M, Watters A, Stetson B, et al.
JAMA Netw Open
. 2023 Jan;
6(1):e2248685.
PMID: 36598790
Importance: Fetal ultrasonography is essential for confirmation of gestational age (GA), and accurate GA assessment is important for providing appropriate care throughout pregnancy and for identifying complications, including fetal growth...
5.
Gomes R, Vwalika B, Lee C, Willis A, Sieniek M, Price J, et al.
Commun Med (Lond)
. 2022 Oct;
2:128.
PMID: 36249461
Background: Fetal ultrasound is an important component of antenatal care, but shortage of adequately trained healthcare workers has limited its adoption in low-to-middle-income countries. This study investigated the use of...
6.
Kazemzadeh S, Yu J, Jamshy S, Pilgrim R, Nabulsi Z, Chen C, et al.
Radiology
. 2022 Sep;
306(1):124-137.
PMID: 36066366
Background The World Health Organization (WHO) recommends chest radiography to facilitate tuberculosis (TB) screening. However, chest radiograph interpretation expertise remains limited in many regions. Purpose To develop a deep learning...
7.
Sellergren A, Chen C, Nabulsi Z, Li Y, Maschinot A, Sarna A, et al.
Radiology
. 2022 Jul;
305(2):454-465.
PMID: 35852426
Background Developing deep learning models for radiology requires large data sets and substantial computational resources. Data set size limitations can be further exacerbated by distribution shifts, such as rapid changes...
8.
Nabulsi Z, Sellergren A, Jamshy S, Lau C, Santos E, Kiraly A, et al.
Sci Rep
. 2021 Sep;
11(1):15523.
PMID: 34471144
Chest radiography (CXR) is the most widely-used thoracic clinical imaging modality and is crucial for guiding the management of cardiothoracic conditions. The detection of specific CXR findings has been the...
9.
Duggan G, Reicher J, Liu Y, Tse D, Shetty S
Br J Radiol
. 2021 Jun;
94(1123):20210435.
PMID: 34142868
Objective: Demonstrate the importance of combining multiple readers' opinions, in a context-aware manner, when establishing the reference standard for validation of artificial intelligence (AI) applications for, chest radiographs. By comparing...
10.
Wulczyn E, Steiner D, Moran M, Plass M, Reihs R, Tan F, et al.
NPJ Digit Med
. 2021 Apr;
4(1):71.
PMID: 33875798
Deriving interpretable prognostic features from deep-learning-based prognostic histopathology models remains a challenge. In this study, we developed a deep learning system (DLS) for predicting disease-specific survival for stage II and...